Sentence Similarity
sentence-transformers
Safetensors
Transformers
bidirlm
feature-extraction
mteb
embedding
bidirectional
custom_code
Instructions to use BidirLM/BidirLM-1B-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BidirLM/BidirLM-1B-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BidirLM/BidirLM-1B-Embedding", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use BidirLM/BidirLM-1B-Embedding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BidirLM/BidirLM-1B-Embedding", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0de7ded1ab340ae8f36385a2482c502ecde5535373412e765aa1e8ffa906e128
- Size of remote file:
- 33.4 MB
- SHA256:
- c455eff9eed1fa5ff7516f571d38590863030c6dbd835c65f35fdc77d21ca3e4
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